Image blur in digital imaging generally is caused by a pixel recording light from multiple locations of a scene. Illustrated in FIG. 1 are three common types of blur: defocus, camera shake and object motion. Defocus blur occurs when light rays originating from the same point on a scene do not converge on the image plane (i.e. the sensor plane), with wider lens apertures resulting in more defocus blur. Camera motion during the exposure produces global motion blur where the same point of the scene is observed by multiple moving pixel sensors. Object motion causes each pixel to observe light from multiple points on the scene, and produces spatially-variant motion blur. Assuming Lambertian surfaces, blur is typically represented by the implied blur kernel that acts on the unobserved, sharp, in-focus image. A blurred image can be mathematically described as the convolution of the in-focus version of the image with a blur kernel.
Blur is a part of everyday photography. Long exposure is needed to overcome poor lighting conditions, but it increases the risk of camera shake and object motion blurs that severely deteriorate the sharpness of the image. Automatic or manual focus is also a challenge when the scene covers a wide range of depths or is rapidly changing (e.g. sports photography), often causing unwanted defocus blur. On the other hand, professional photographers use well-controlled blur to enhance the aesthetics of a photograph. Thus, the ability to manipulate blur in postprocessing would offer greater flexibility in consumer and professional photography.
Blur is also important for computer vision such as bar code scanners (1D and 2D) and other machine vision implementations. Blur may vary across the spatial location (e.g. a scene with multiple moving objects or multiple depths) or be global (e.g. camera shake). The blur resulting from camera shake or object motion can provide valuable information regarding the temporal state of the camera and the scene. The defocus blur kernel varies with the object distance/depth, which can be useful for three-dimensional scene retrieval from a single camera. Blur also interferes with recognition tasks, as feature extraction from a blurry image is a real challenge.
Recent advancements on blind and non-blind deblurring have enabled the handling of complex uniform blur kernels. By comparison, progress in blind and non-blind deblurring for spatially varying blur kernels has been slow, since there is limited data availability to support localized blur kernels. For this reason, it is more common to address this problem using multiple input images and additional hardware. Approaches to computational solutions include supervised or unsupervised foreground/background segmentation, statistical modeling, and partial differential equation (PDE) methods. In particular, sparsifying transforms have played key roles in the detection of blur kernels—gradient operator, shock filter, and wavelet transforms have been used for this purpose. However, existing works have shortcomings, such as problems with ringing artifacts in deblurring or the inability to handle spatially varying blur. It is also common for deblurring algorithms to require multiple iterations, which is highly undesirable for many real-time applications.
Accordingly, to detect motion blur and reconstruct a sharp image, it is desirable to have a method for estimating a blur kernel from a captured blurry image. It is desirable that the method provide for fast, accurate recreation of the latent sharp image from the captured blurry image. Additionally, it is desirable to have a method for analyzing a blurry image which can reconstruct the latent sharp image regardless of how the blur was created, and which can process both global and spatially variant blurs.
The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be carried out in a variety of other ways, including those not necessarily depicted in the drawings. The accompanying drawings incorporated in and forming a part of the specification illustrate several aspects of embodiments of the present invention, and together with the description, serve to explain the principles of the invention; it being understood, however, that this invention is not limited to the precise arrangements shown.